Abstract
BackgroundThere is a growing emphasis on the need to engage patients in order to improve the quality of health care and improve health outcomes. However, we are still lacking a comprehensive understanding on how different measures of patient experiences interact with one another or relate to health status. This study takes a network perspective to 1) study the associations between patient characteristics and patient experience in health care and 2) identify factors that could be prioritized to improve health status.MethodsThis study uses data from the two-year panels from the Medical Expenditure Panel Survey (MEPS) initiated between 2004 and 2011 in the United States. The 88 variables regarding patient health and experience with health care were identified through the MEPS documentation. Sex, age, race/ethnicity, and years of education were also included for analysis. The bnlearn package within R (v3.20) was used to 1) identify the structure of the network of variables, 2) assess the model fit of candidate algorithms, 3) cross-validate the network, and 4) fit conditional probabilities with the given structure.ResultsThere were 51,023 MEPS interviewees aged 18 to 85 years (mean = 44, 95% CI = 43.9 to 44.2), with years of education ranging from 1 to 19 (mean = 7.4, 95% CI = 7.40 to 7.46). Among all, 55% and 74% were female and white, respectively. There were nine networks identified and 17 variables not linked to others, including death in the second years, sex, entry years to the MEPS, and relations of proxies. The health status in the second years was directly linked to that in the first years. The health care ratings were associated with how often professionals listened to them and whether professionals’ explanation was understandable.ConclusionsIt is feasible to construct Bayesian networks with information on patient characteristics and experiences in health care. Network models help to identify significant predictors of health care quality ratings. With temporal relationships established, the structure of the variables can be meaningful for health policy researchers, who search for one or a few key priorities to initiate interventions or health care quality improvement programs.
Highlights
There is a growing emphasis on the need to engage patients in order to improve the quality of health care and improve health outcomes
This study aims to 1) construct a Bayesian network model with individual characteristics and commonly used measures of patient experience, especially quality of care in the Consumer Assessment of Health Plans Study (CAHPS), and 2) illustrate the relationships between patient experience and health outcomes through graphics and probability distributions
This study uses data from the Medical Expenditure Panel Survey (MEPS) that was implemented with a focus on both self-perceived health status and patient experience with health care [13]
Summary
There is a growing emphasis on the need to engage patients in order to improve the quality of health care and improve health outcomes. Experiences in the timeliness and perceived quality of health care and communication with physicians are measured with the Consumer Assessment of Health Plans Study (CAHPS) questionnaire [2]. One of the issue is that patient experiences may be simplified into a single dimension [7] This may oversimplify the complexity of patient experiences in health care and overlook the opportunities that we can take advantage of to improve healthcare quality and patient health [7]. The communication with providers other than doctors may not be considered while assessing patient experience [7] This can underestimate the input and effectiveness of patientprovider communication to improve patient health [7]
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